Semiconductor manufacture is becoming an increasingly automated process requiring precise methods of process control to ensure a quality output. Since the process is automated, safeguards are required to protect the manufacturing system and acknowledge when the manufacturing system, or tool, is unstable and is performing poorly.
Typically, there are only several factors that are measured during the semiconductor manufacturing process, for example, thickness of a film after the film has been deposited, polished, and/or etched. Because of this, occasionally there will be situations in which the tool performance changes due to factors that are not directly measured. For example, one factor that is not directly monitored that can cause failures in a semiconductor device is an increased amount of particles on the wafer, where the increase in particles is caused by an increase in the pressure in a chamber where the manufacturing process is being performed. As the manufacturing process is designed, an experiment may be conducted to determine how many particles are introduced based on various levels of pressure. Since particles cannot be measured while the manufacturing process is executing, the designer must assume that the model is correct.
For situations in which there is no automated control, changes in the process performance as a result of errors in the model may be directly observed in the wafer properties. In the particles example, when the pressure in the chamber increases, the increase in the amount of particles on the wafer may be directly observed as a change in the thickness of the wafer. A human controller would notice the change in the wafer thickness and, in examining the process to determine the source of the thickness increase, would notice that the pressure had changed. The human controller would also perhaps notice that the change in pressure had caused the increase in the number of damaging particles on the wafer.
When advanced process control (“APC”) techniques are applied, however, the APC methodology attempts to compensate for any changes in the manufacturing process and such changes may not be as easily observed. In the particles example, the thickness of the wafer is regulated, such that, when the model has predicted perfectly the required pressure in the chamber, as the pressure changes during execution of the process the thickness of the wafer does not change. However, when the model is not correctly predicting the behavior of the process, these pressure variations may cause an increase in particles to occur. However, although particles are being introduced and are damaging the wafer, the APC will not automatically detect these variations in pressure (i.e. the APC only detects an increase in the thickness of the wafer).
Thus, the use of advanced process control methods demonstrates a need for examining the behavior of the process in the context of a process that is being controlled. Two types of monitoring techniques, for example, process health monitoring and model health monitor, are often used to fulfill this need.
Process health monitoring may be used to effectively monitor, for example, an automated process that is under computer control. Process health monitoring detects deviation of controlled outputs of the process, or tool, away from some predetermined target area. Process health monitoring may, itself, be an automated procedure. Process health monitoring methods provide high-level information for, for example, each controlled output of a process. For example, process health monitoring may be applied to chemical mechanical planarization (“CMP”), chemical vapor deposition (“CVD”), etching, electrochemical plating processes, (“ECP”), physical vapor deposition (“PVD”), etc. Such monitoring is accomplished by taking measurements of the process parameters that are of concern, then, to perform statistical analysis of those measurements, and, finally, to compare the statistical analysis to desired limits. Thus, a determination is made as to whether any specified controlled output(s) has strayed too far from a predetermined target.
Model health monitoring, which may be used to monitor each run-to-run (“R2R”) control model for CMP, CVD, ECP, PVD, etc., detects deviation of, for example, the R2R model from the actual behavior of the process, or tool. Model health monitoring also may be an automated procedure. In the case of model health monitoring, the statistical analyses may include such pertinent information as model predictions and necessary previous data to perform these model predictions. Health monitoring may, itself, be an automated procedure.
Prior methods of process and model health monitoring employed indices relating to such monitoring. However, prior methods of monitoring were used for continuous processes such as, for example, controller performance monitoring. Controller performance monitoring looks at a desired, best controller performance based on specific data, which are calculated using time series analysis, and takes a ratio of a current variance to the minimum variance controller performance. However, unlike with semiconductor manufacturing processes, controller performance monitoring takes into account only the continuous process, rather than monitoring distinct points in the process.
A continuous process, in general, refers to a process that is run in a mode where things are constantly coming in and constantly going out. A simple example is a tank that has fluid coming in and fluid going out. In a continuous process, the goal is to continually maintain the process in a certain state. For example, in the case of the tank, the goal would be to control the rate at which fluid is being pumped into, or out of, the tank such that the level of fluid in the tank is maintained at a constant level.
Controller performance monitoring is performed using minimum variance control theory for systems that have dynamics. In other words, the dynamic process is monitored only to determine what factors are affecting the maintenance of the continuous, on-going process. Prior methods of process and model health monitoring made use of the dynamic equations that are used to do control of such continuous processes.
In contrast, semiconductor processes are usually modeled as static processes for the purposes of run-to-run control. Rather than the manufacturing of a wafer being a continuous process, once a wafer is completed, the process is over. The process, itself, is repeated for subsequent wafers without being altered. A static, or discrete, process such as manufacturing a wafer can only be monitored in terms of how the process performed for prior, discrete manufacturing occurrences. An action in a static process (for example, a deposition time change or change in polish time), which occurred on the previous three wafers, may not have much of an effect on the processing of the subsequent wafer. Such static processes lack the dynamic equations used to model continuous process and, therefore, the model and process health monitoring techniques utilized for continuous process cannot be employed in monitoring static processes, e.g., semiconductor manufacturing.
What is desired is a method and system that allows a controller to monitor the performance of a static manufacture process during the entire cycle of the process such as to maintain the performance of the process as the process is repeated.